249 citations · 790 across the 59 of their papers we have counts for
11 papers · 1 filter
A learning-based algorithm to quickly compute good primal solutions for Stochastic Integer Programs
Yoshua Bengio, Emma Frejinger, Andrea Lodi +2
We propose a novel approach using supervised learning to obtain near-optimal primal solutions for two-stage stochastic integer programming (2SIP) problems with constraints in the f…
Disjunctive cuts in Mixed-Integer Conic Optimization
Andrea Lodi, Mathieu Tanneau, Juan Pablo Vielma
This paper studies disjunctive cutting planes in Mixed-Integer Conic Programming. Building on conic duality, we formulate a cut-generating conic program for separating disjunctive…
On Generalized Surrogate Duality in Mixed-Integer Nonlinear Programming
Benjamin Müller, Gonzalo Muñoz, Maxime Gasse +3
The most important ingredient for solving mixed-integer nonlinear programs (MINLPs) to global epsilon-optimality with spatial branch and bound is a tight, computationally tractable…
JANOS: An Integrated Predictive and Prescriptive Modeling Framework
David Bergman, Teng Huang, Philip Brooks +2
Business research practice is witnessing a surge in the integration of predictive modeling and prescriptive analysis. We describe a modeling framework JANOS that seamlessly integra…
Game theoretical analysis of Kidney Exchange Programs
Margarida Carvalho, Andrea Lodi
The goal of a kidney exchange program (KEP) is to maximize number of transplants within a pool of incompatible patient-donor pairs by exchanging donors. A KEP can be modelled as a…
Learning chordal extensions
Defeng Liu, Andrea Lodi, Mathieu Tanneau
A highly influential ingredient of many techniques designed to exploit sparsity in numerical optimization is the so-called chordal extension of a graph representation of the optimi…
When Nash Meets Stackelberg
Margarida Carvalho, Gabriele Dragotto, Felipe Feijoo +2
This article introduces a class of games among players (), namely, a class of simultaneous non-cooperative games where the players solve sequential Stac…
Learning to Handle Parameter Perturbations in Combinatorial Optimization: an Application to Facility Location
Andrea Lodi, Luca Mossina, Emmanuel Rachelson
We present an approach to couple the resolution of Combinatorial Optimization problems with methods from Machine Learning, applied to the single source, capacitated, facility locat…
Exact Combinatorial Optimization with Graph Convolutional Neural Networks
Maxime Gasse, Didier Chételat, Nicola Ferroni +2
Combinatorial optimization problems are typically tackled by the branch-and-bound paradigm. We propose a new graph convolutional neural network model for learning branch-and-bound…
Activation Adaptation in Neural Networks
Farnoush Farhadi, Vahid Partovi Nia, Andrea Lodi
Many neural network architectures rely on the choice of the activation function for each hidden layer. Given the activation function, the neural network is trained over the bias an…
Predicting Tactical Solutions to Operational Planning Problems under Imperfect Information
Eric Larsen, Sébastien Lachapelle, Yoshua Bengio +3
This paper offers a methodological contribution at the intersection of machine learning and operations research. Namely, we propose a methodology to quickly predict tactical soluti…